Diabetic retinopathy (DR) is a microvascular disease characterized by hyper-permeability, capillary occlusion, and neovascularization. These pathophysiologic changes can lead to macular edema and proliferative diabetic retinopathy, which are responsible for most of the vision loss associated with DR. Therefore early detection and monitoring of these complications is important in preventing permanent visual impairment.
Optical coherence tomography (OCT) is a noninvasive volumetric imaging technique that uses principles of interferometry to provide depth-resolved cross-sectional and three-dimensional (3D) imaging of biological tissues. OCT can provide millimeter level penetration into the target tissue and has micrometer-scale axial and lateral resolution, making it well-suited for the characterization of microstructural features. In clinical ophthalmology, OCT is commonly used to detect diabetic macular edema (DME), a condition wherein leakage of fluid from blood vessels causes fluid accumulation in the central part of the retina (the macula). Response to treatment for DME is typically assessed by mapping the total retinal thickness and/or retinal volume, and current OCT platforms provide specific functionality to measure these quantities. However, these structural indices are not always good indicators of disease status and progression, because they combine the contributions of both retinal tissue and accumulated retinal fluid into a single composite measurement. This is problematic because retinal thickness and volume are influenced not only by vascular permeability, but by numerous other factors including ischemic atrophy and fibrosis. Thus, while retinal thickness and volume may increase with vascular leakage, they can also decrease due to ischemic atrophy in the setting of DME. This multiplicity of influences confounds attempts to correlate retinal thickness and volume measurements with vascular permeability in DME and other related disease states.
As an alternative to thickness and volume measurements, a direct quantification of fluid volume within the retina, including intraretinal fluid (IRF) and subretinal fluid (SRF), would provide a more robust and accurate biomarker of disease activity. Indeed, the clinical relevance of IRF and SRF on OCT is well established. Resolution or stabilization of IRF and SRF has been used as a main indicator of disease activity in numerous studies of DME, neovascular aged-related macular degeneration (AMD), and retinal vein occlusion. In such studies, direct detection of retinal fluid has been performed qualitatively through visual inspection of sequential OCT cross sections but the process is laborious and prone to operator bias and error. Thus, an automated method that detects and quantifies retinal fluid volume is needed to make analysis of retinal fluid accumulation practical in clinical settings.
While a few state-of-the-art algorithms to provide fluid segmentation on clinical two dimensional (2D) OCT images with DME have been presented, there remains a need for a robust automated method. Despite its clear applicability, automated detection of volumetric retinal fluid has been a poorly explored area in OCT. No commercial system offers this function, leaving the identification of fluid space to subjective assessment or to time-intensive manual delineation. An active contour approach (i.e., a gradient vector flow snake model) has been applied to extract fluid regions in retinal structure of AMD patients, but the method is slow and requires substantial grader input, including initial boundary location estimation. An approach has also been described for automated segmentation of retinal fluid in cystoid macular edema using a Cirrus OCT system. This method applies an empirical thresholding cutoff on the contrast enhance images by bilateral filtering, but sparse details were presented on the assessment of the segmentation reliability. A fully automated algorithm based on a kernel regression classification method has been presented to identify fluid-filled region in real world spectral domain OCT images of eyes with severe DME. However, this algorithm did not distinguish between IRF and SRF, nor did it distinguish focal from diffuse retinal thickening. Moreover, all of the aforementioned algorithms were developed for 2D OCT images only, not three dimensional (3D) volumetric datasets. Volumetric approaches have been introduced which use prior information to classify the fluid associated abnormalities based on feature- and layer-specific properties in comparison with the normal appearance of macula, but these methods are unable to provide a clean measurement of fluid-filled space and are not suited to clinical use.
Thus, there remains a need for a fully automated segmentation method to identify and quantify fluid-filled regions of the retina and subretina in a 3D OCT dataset. Such a tool would significantly enhance the clinical management of macular diseases associated with hyper-permeability.